Clinical study for classification of benign, dysplastic, and malignant oral lesions using autofluorescence spectroscopy
Clinical study for classification of benign, dysplastic, and malignant oral lesions using autofluorescence spectroscopy
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DOI：
10.1117/1.1782611
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发表时间：
2004-09-01
影响因子：
3.5
通讯作者：
Roodenburg, JLN
中科院分区：
文献类型：
--
作者：
de Veld, DCG;Skurichina, M;Roodenburg, JLN
自体荧光光谱法显示了口腔（前）恶性肿瘤的检测和分期的有前途的结果。为了提高分期的可靠性，我们开发和比较病变分类算法。此外，我们还研究了检测不可见组织变化的潜力。在六个激发波长下记录来自172个良性、发育异常和癌性病变分析（PCA）、人工神经网络和红/绿色强度比的自体荧光光谱，以使用四种归一化技术将良性病变与（前）恶性病变分开。为了评估检测不可见组织改变的潜力，我们比较了健康粘膜和病变周围/对侧位置的PC评分。光谱显示每个病变组内的形状和强度的大变化。强度和PC评分分布表明良性和（前）恶性病变之间有很大的重叠。用于区分癌组织和健康组织的受试者-操作者特征曲线下面积（ROC-AUC）非常好（0.90至0.97）。然而，对于所有方法（0.50至0.70），ROC-AUC对于良性与（癌前）恶性粘膜的分类来说都太低了。观察到良性和健康组织以及（前）恶性病变的周围/对侧组织之间的一些统计学显著差异。我们可以成功地将健康粘膜与癌症分离（R 0 C-AUC>0.9）。然而，自体荧光光谱不能使用我们的方法区分良性病变和可见的（前）恶性病变（ROC-AUC
Autofluorescence spectroscopy shows promising results for detection and staging of oral (pre-)malignancies. To improve staging reliability, we develop and compare algorithms for lesion classification. Furthermore, we examine the potential for detecting invisible tissue alterations. Autofluorescence spectra are recorded at six excitation wavelengths from 172 benign, dysplastic, and cancerous lesions analysis (PCA), artificial neural networks, and red/green intensity ratio's to separate benign from (pre-)malignant lesions, using four normalization techniques. To assess the potential for detecting invisible tissue alterations, we compare PC scores of healthy mucosa and surroundings/contralateral positions of lesions. The spectra show large variations in shape and intensity within each lesion group. Intensities and PC score distributions demonstrate large overlap between benign and (pre-)malignant lesions. The receiver-operator characteristic areas under the curve (ROC-AUCs) for distinguishing cancerous from healthy tissue are excellent (0.90 to 0.97). However, the ROC-AUCs are too low for classification of benign versus (pre-)malignant mucosa for all methods (0.50 to 0.70). Some statistically significant differences between surrounding/contralateral tissues of benign and healthy tissue and of (pre-)malignant lesions are observed. We can successfully separate healthy mucosa from cancers (ROC-AUC>0.9). However, autofluorescence spectroscopy is not able to distinguish benign from visible (pre-)malignant lesions using our methods (ROC-AUC